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Ahlmann-Eltze, Huber and Anders (Nature Methods, published online 4 August 2025) report that none of five foundation models and two other deep-learning models outperformed deliberately simple baselines at predicting transcriptome changes after genetic perturbations, and conclude that the goal of predicting the outcome of not-yet-performed experiments is still elusive. Copy claim link
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Date 2025-08-04 · publication Records dated 2025 → Role Contesting Register (kind of text) Nonfiction Scope of the passage Challenges the present performance of the tested models; not a contradiction of the proposition as a whole. Scope and limits The record documents what this text states, in its register; it is not evidence that the proposition is true and implies no influence on or from any other text. A qualified challenge: the paper reports that the models it tested did not outperform deliberately simple baselines on its benchmarks, and concludes that a stated goal of such models is still elusive. It challenges the present performance of those models; it does not contradict the proposition as a whole, which does not require outperforming simple baselines and includes models that will be able to predict cell behaviour (cross-family check, item B119; maintainer decision of 2026-10-30).
The evidence 02 primary source Supports the claim
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines ↗ Nature Methods · 2025-08-04
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Source excerpt “Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines” Location in source Abstract
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Machine-checked · 2026-10-30
Source details and check notes Authors Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders Version Retrieved for the candidate pack on 2026-10-30 (SHA-256 3ad786d9b77f25dae848ab4354e643e1327a3ca8b792a4d577caf75654567656). Nature Methods article page, version of record of 4 August 2025, as served on 2026-10-30. Canonical address Open the canonical source ↗ Accessed 2026-10-30 Language English (en) Attribution Constantin Ahlmann-Eltze, Wolfgang Huber, Simon Anders, “Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines”, 2025-08-04. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (29 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. The authors' reported result. A cross-family machine check (2026-10-30, item B119) re-read the record’s sources and found full support. No human editorial review. primary source Supports the claim
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines ↗ Nature Methods · 2025-08-04
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Source excerpt “we conclude that the latter's goal of providing a generalizable representation of cellular states and predicting the outcome of not-yet-performed experiments is still elusive.” Location in source Main text, after the comparison results
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Constantin Ahlmann-Eltze; Wolfgang Huber; Simon Anders Version Retrieved for the candidate pack on 2026-10-30 (SHA-256 3ad786d9b77f25dae848ab4354e643e1327a3ca8b792a4d577caf75654567656). Nature Methods article page, version of record of 4 August 2025, as served on 2026-10-30. Canonical address Open the canonical source ↗ Accessed 2026-10-30 Language English (en) Attribution Constantin Ahlmann-Eltze, Wolfgang Huber, Simon Anders, “Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines”, 2025-08-04. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (24 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. The authors' conclusion on the stated goal. A cross-family machine check (2026-10-30, item B119) re-read the record’s sources and found full support. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document disputes the proposition or a premise it depends on. The record documents the text’s position, not who is right.
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Record ID aib-arg-p2-ahlmann-eltze-2025